{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning with Decision Trees and Random Forests\n",
    "\n",
    "\n",
    "In this project we will be exploring publicly available data from LendingClub.com. Lending Club is a service that connects borrowers with people who have money (investors). We will try to create a model that will help predict if a person has a high probability of paying the loan back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Data\n",
    "\n",
    "We will use lending data from 2007-2010 and try to classify and predict whether or not the borrower paid back their loan in full. \n",
    "\n",
    "The data set contains the following features:\n",
    "\n",
    "* credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n",
    "* purpose: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n",
    "* int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n",
    "* installment: The monthly installments owed by the borrower if the loan is funded.\n",
    "* log.annual.inc: The natural log of the self-reported annual income of the borrower.\n",
    "* dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n",
    "* fico: The FICO credit score of the borrower.\n",
    "* days.with.cr.line: The number of days the borrower has had a credit line.\n",
    "* revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n",
    "* revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n",
    "* inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.\n",
    "* delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n",
    "* pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Reading the data\n",
    "loans = pd.read_csv('data/loan_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9.578000e+03</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.804970</td>\n",
       "      <td>0.122640</td>\n",
       "      <td>319.089413</td>\n",
       "      <td>10.932117</td>\n",
       "      <td>12.606679</td>\n",
       "      <td>710.846314</td>\n",
       "      <td>4560.767197</td>\n",
       "      <td>1.691396e+04</td>\n",
       "      <td>46.799236</td>\n",
       "      <td>1.577469</td>\n",
       "      <td>0.163708</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>0.160054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.396245</td>\n",
       "      <td>0.026847</td>\n",
       "      <td>207.071301</td>\n",
       "      <td>0.614813</td>\n",
       "      <td>6.883970</td>\n",
       "      <td>37.970537</td>\n",
       "      <td>2496.930377</td>\n",
       "      <td>3.375619e+04</td>\n",
       "      <td>29.014417</td>\n",
       "      <td>2.200245</td>\n",
       "      <td>0.546215</td>\n",
       "      <td>0.262126</td>\n",
       "      <td>0.366676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103900</td>\n",
       "      <td>163.770000</td>\n",
       "      <td>10.558414</td>\n",
       "      <td>7.212500</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>3.187000e+03</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122100</td>\n",
       "      <td>268.950000</td>\n",
       "      <td>10.928884</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>4139.958333</td>\n",
       "      <td>8.596000e+03</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140700</td>\n",
       "      <td>432.762500</td>\n",
       "      <td>11.291293</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>5730.000000</td>\n",
       "      <td>1.824950e+04</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       credit.policy     int.rate  installment  log.annual.inc          dti  \\\n",
       "count    9578.000000  9578.000000  9578.000000     9578.000000  9578.000000   \n",
       "mean        0.804970     0.122640   319.089413       10.932117    12.606679   \n",
       "std         0.396245     0.026847   207.071301        0.614813     6.883970   \n",
       "min         0.000000     0.060000    15.670000        7.547502     0.000000   \n",
       "25%         1.000000     0.103900   163.770000       10.558414     7.212500   \n",
       "50%         1.000000     0.122100   268.950000       10.928884    12.665000   \n",
       "75%         1.000000     0.140700   432.762500       11.291293    17.950000   \n",
       "max         1.000000     0.216400   940.140000       14.528354    29.960000   \n",
       "\n",
       "              fico  days.with.cr.line     revol.bal   revol.util  \\\n",
       "count  9578.000000        9578.000000  9.578000e+03  9578.000000   \n",
       "mean    710.846314        4560.767197  1.691396e+04    46.799236   \n",
       "std      37.970537        2496.930377  3.375619e+04    29.014417   \n",
       "min     612.000000         178.958333  0.000000e+00     0.000000   \n",
       "25%     682.000000        2820.000000  3.187000e+03    22.600000   \n",
       "50%     707.000000        4139.958333  8.596000e+03    46.300000   \n",
       "75%     737.000000        5730.000000  1.824950e+04    70.900000   \n",
       "max     827.000000       17639.958330  1.207359e+06   119.000000   \n",
       "\n",
       "       inq.last.6mths  delinq.2yrs      pub.rec  not.fully.paid  \n",
       "count     9578.000000  9578.000000  9578.000000     9578.000000  \n",
       "mean         1.577469     0.163708     0.062122        0.160054  \n",
       "std          2.200245     0.546215     0.262126        0.366676  \n",
       "min          0.000000     0.000000     0.000000        0.000000  \n",
       "25%          0.000000     0.000000     0.000000        0.000000  \n",
       "50%          1.000000     0.000000     0.000000        0.000000  \n",
       "75%          2.000000     0.000000     0.000000        0.000000  \n",
       "max         33.000000    13.000000     5.000000        1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>purpose</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Using quick visualisations, we can explore the relationship between different variables in the dataset. \n",
    "\n",
    "Let's start with a dual histogram of the FICO score of the borrowers, depending on the credit policy (i.e. if a borrower met the underlying criteria)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115466a20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IZMdfd1cuIiIiA4yTJCIEDAVmW2t3ABhjxgGXAc8DB7epn00swQBooH3CkA1U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\nSZIasYiQJEmNWERIkqRG/gH88XVv1ETTaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115291908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loans[loans['credit.policy']==1]['fico'].hist(bins=30,alpha=0.6,label='Credit.Policy=1')\n",
    "loans[loans['credit.policy']==0]['fico'].hist(bins=30,alpha=0.6,label='Credit.Policy=0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking out a similar histogram, based on the 'not.fully.paid' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1159bd710>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FiESiNDQ4b0ZQzEuvv8c8EokSrhrK4JrSJBHZx2QxZMb8s8+aSSSS3bb36acN7LffAVRV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\neVU1afZlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a3ec550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loans[loans['not.fully.paid']==1]['fico'].hist(bins=30,alpha=0.6,label='not.fully.paid=1',color='red')\n",
    "loans[loans['not.fully.paid']==0]['fico'].hist(bins=30,alpha=0.6,label='not.fully.paid=0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the counts of of loan purposes, based on whether a borrower fully paid the loan back or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vSJIkSZKqr6dH8oiIOmB/YFuyWydMB/6YUmqtUG2SJEmSpG7q6TV5w4E7gO2B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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115460be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "sns.countplot(loans['purpose'],hue=loans['not.fully.paid'])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the trend between FICO score and interest rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x115460898>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h7blzse71/egd8CIzzYU1585FUU4a2tu7MaskR/h1s0py0N7eDQBYPLcQX/nU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\nLhRtUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154606d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fico',y='int.rate',data=loans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _purpose_ column is categorical.\n",
    "\n",
    "That means we need to transform them using dummy variables so sklearn will be able to understand them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Creating a list of all categorical features.\n",
    "cat_feats = ['purpose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['credit.policy', 'int.rate', 'installment', 'log.annual.inc', 'dti',\n",
       "       'fico', 'days.with.cr.line', 'revol.bal', 'revol.util',\n",
       "       'inq.last.6mths', 'delinq.2yrs', 'pub.rec', 'not.fully.paid',\n",
       "       'purpose_credit_card', 'purpose_debt_consolidation',\n",
       "       'purpose_educational', 'purpose_home_improvement',\n",
       "       'purpose_major_purchase', 'purpose_small_business'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = final_data.drop('not.fully.paid',axis=1)\n",
    "y = final_data['not.fully.paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training a Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtree= DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluation of Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      0.83      0.85      2432\n",
      "          1       0.20      0.23      0.21       442\n",
      "\n",
      "avg / total       0.75      0.74      0.75      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2030  402]\n",
      " [ 342  100]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluation of Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.98      0.91      2432\n",
      "          1       0.30      0.06      0.10       442\n",
      "\n",
      "avg / total       0.77      0.83      0.78      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2372   60]\n",
      " [ 416   26]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the random forest performed somewhat better than the decision tree model."
   ]
  }
 ],
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